Train a linear regression model with no regularization using Stochastic Gradient Descent.
This solves the least squares regression formulation
f(weights) = 1/n ||A weights-y||^2^
(which is the mean squared error).
Here the data matrix has n rows, and the input RDD holds the set of rows of A, each with
its corresponding right hand side label y.
See also the documentation for the precise formulation.